zettaquant-vslm-mcp

MCP server that exposes ZettaQuant V-SLM to any MCP-aware LLM host.

Add three lines to your Claude Desktop / Cursor / Zed / Windsurf config and your model can autonomously filter noisy context (earnings-call transcripts, news articles, long reports, log lines) using ZettaQuant's topic-conditioned relevancy classifier — typically cutting token spend 5-10× before the LLM even sees the input.

What it exposes

One tool, over stdio, via the Model Context Protocol:

The LLM decides when to call it on its own — no code you have to write in the host.

Install & configure

You need a ZettaQuant API key with the vslm scope. Get one at zettaquant.ai (or ask your account contact).

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "zettaquant-vslm": {
      "command": "uvx",
      "args": ["zettaquant-vslm-mcp"],
      "env": {
        "ZQ_API_KEY": "<your api key>"
      }
    }
  }
}

Restart Claude Desktop. The tools appear under the "Tools" menu.

Cursor

Edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "zettaquant-vslm": {
      "command": "uvx",
      "args": ["zettaquant-vslm-mcp"],
      "env": { "ZQ_API_KEY": "<your api key>" }
    }
  }
}

Restart Cursor.

Zed

In ~/.config/zed/settings.json:

{
  "context_servers": {
    "zettaquant-vslm": {
      "command": {
        "path": "uvx",
        "args": ["zettaquant-vslm-mcp"],
        "env": { "ZQ_API_KEY": "<your api key>" }
      }
    }
  }
}

Windsurf / other MCP hosts

Any host that supports the standard mcpServers config shape works — use the same command + args + env block as above.

Environment variables

Usage examples in the LLM

Once installed, just ask naturally. Some prompts that will trigger vslm_predict:

• "Here are 40 sentences from Apple's Q3 earnings call. Pull out the ones about AI capex plans."
• "Filter these log lines down to anything related to lateral movement."
• "I pasted a 10-K risk section. Only show me sentences about supply-chain exposure."

The model calls vslm_predict under the hood, gets back the relevant sentences, and works from those — cheaper and more precise than reading the full input.

What is MCP?

The Model Context Protocol is an open standard (released by Anthropic, November 2024) for connecting LLM hosts to external tools and data. It's essentially "USB-C for AI apps" — write one integration and every MCP-aware host can use it. This package is a server in MCP parlance; the LLM host is the client.

Development

uv venv
uv pip install -e ".[dev]"
export ZQ_API_KEY="..."
python -m zettaquant_vslm_mcp

The server speaks JSON-RPC on stdin/stdout. To poke it manually, use the MCP Inspector:

npx @modelcontextprotocol/inspector uvx zettaquant-vslm-mcp

Troubleshooting

• "ZQ_API_KEY is not set" in the host logs → the env block in your MCP config didn't propagate. Confirm the config file path and restart the host.
• 401 in tool output → key is valid but wrong. Try it directly: curl -H "x-api-key: $ZQ_API_KEY" https://api.zettaquant.ai/v1/usage/me.
• 403 SCOPE_DENIED → your key doesn't have the vslm scope. Contact ZettaQuant.
• 429 QUOTA_EXCEEDED → you hit your per-period cap; the error message includes the reset time.

License

MIT.